How to Build a Top-Notch AI Software Development Team
Sharing insightful tips to help you better plan your AI-first strategy and AI software development endeavor.
Five Steps To Building an AI Chatbot That Drives Value and Converts
Explore the difference between transactional and conversational chatbots and some common approaches to building highly-efficient AI chatbots that drive value and convert.
AI Chatbots in FinTech: From Capital Management To Financial Advisory
By 2020, 85% of all supplier dialogues with customers will be held without live contact with human beings. Let's see how it affects FinTech.
Staff Augmentation As a Driver of AI Startups Ecosystem Evolution
40% of European AI startups are fake, according to a recent study. The lack of AI talent onshore slows down any AI R&D, product development and launch processes
Global AI Skills Crisis And How Different Nations Tackle The Issue
As the research shows, there are only 300,000 AI researchers and practitioners worldwide today, with the market demanding millions of jobs to bridge the skills gap. Check out what it takes to grow a new generation of AI software developers and how to fill the skills gap now while many nations have yet to realize the transformative power of AI.
Top 8 Tools and Libraries For ML and DL Project Development
Check out out tips about what languages and libraries to use for your ML and DL project development.
Three Challenges In Machine Learning Development and One Way to Overcome Them
Let’s take a look at top 3 challenges facing companies seeking to jump fast on the ML technology bandwagon and derive substantial business value from it, and let me show you on real-life examples how ML outsourcing can be a good way to overcome all 3 challenges.
Machine Learning In FinTech: From Manipulation Detection to Stock Market Price Predictions
As progressive technologies, personalization, artificial intelligence, and Big Data gain momentum, traditional banking and financial systems undergo a major overhaul.
How to Optimize DevOps With Machine Learning
Most of the data created in the process of DevOps is related directly to the application deployment. Application monitoring replenishes server logs, generates error messages, and transaction tracing. The only reasonable way to analyze this data and make the right conclusions in real time is to use machine learning (ML).
Machine Learning In a Nutshell: When, Why and How
All you need to know about machine learning, its types, and approaches.
